78 citations · 193 across the 14 of their papers we have counts for
15 papers
AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data
Tuan Truong, Rithwik Sudharsan, Yibo Yang +4
The site conditions that make astronomical observatories in space and on the ground so desirable -- cold and dark -- demand a physical remoteness that leads to limited data transmi…
Fast Samplers for Inverse Problems in Iterative Refinement Models
Kushagra Pandey, Ruihan Yang, Stephan Mandt
Constructing fast samplers for unconditional diffusion and flow-matching models has received much attention recently; however, existing methods for solving inverse problems, such a…
Precipitation Downscaling with Spatiotemporal Video Diffusion
Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan +4
In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statisti…
CMMD: Contrastive Multi-Modal Diffusion for Video-Audio Conditional Modeling
Ruihan Yang, Hannes Gamper, Sebastian Braun
We introduce a multi-modal diffusion model tailored for the bi-directional conditional generation of video and audio. We propose a joint contrastive training loss to improve the sy…
Lossy Image Compression with Conditional Diffusion Models
Ruihan Yang, Stephan Mandt
This paper outlines an end-to-end optimized lossy image compression framework using diffusion generative models. The approach relies on the transform coding paradigm, where an imag…
Diffusion Probabilistic Modeling for Video Generation
Ruihan Yang, Prakhar Srivastava, Stephan Mandt
Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability t…
SC2 Benchmark: Supervised Compression for Split Computing
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato +1
With the increasing demand for deep learning models on mobile devices, splitting neural network computation between the device and a more powerful edge server has become an attract…
Supervised Compression for Resource-Constrained Edge Computing Systems
Yoshitomo Matsubara, Ruihan Yang, Marco Levorato +1
There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors. However, full-scale deep neural netwo…
Insights from Generative Modeling for Neural Video Compression
Ruihan Yang, Yibo Yang, Joseph Marino +1
While recent machine learning research has revealed connections between deep generative models such as VAEs and rate-distortion losses used in learned compression, most of this wor…
Hierarchical Autoregressive Modeling for Neural Video Compression
Ruihan Yang, Yibo Yang, Joseph Marino +1
Recent work by Marino et al. (2020) showed improved performance in sequential density estimation by combining masked autoregressive flows with hierarchical latent variable models.…
PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
Ziyu Wang, Yiyi Zhang, Yixiao Zhang +4
The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE). However, most, if not all, viable attempts on thi…
Deep Music Analogy Via Latent Representation Disentanglement
Ruihan Yang, Dingsu Wang, Ziyu Wang +3
Analogy-making is a key method for computer algorithms to generate both natural and creative music pieces. In general, an analogy is made by partially transferring the music abstra…
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy
Ruihan Yang, Qiwei Ye, Tie-Yan Liu
A fundamental issue in reinforcement learning algorithms is the balance between exploration of the environment and exploitation of information already obtained by the agent. Especi…
Inspecting and Interacting with Meaningful Music Representations using VAE
Ruihan Yang, Tianyao Chen, Yiyi Zhang +1
Variational Autoencoders(VAEs) have already achieved great results on image generation and recently made promising progress on music generation. However, the generation process is…
MatchBench: An Evaluation of Feature Matchers
JiaWang Bian, Ruihan Yang, Yun Liu +4
Feature matching is one of the most fundamental and active research areas in computer vision. A comprehensive evaluation of feature matchers is necessary, since it would advance bo…